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NASA NTRS2001

Estimates of Ice Sheet Mass Balance from Satellite Altimetry: Past and Future

A major uncertainty in predicting sea level rise is the sensitivity of ice sheet mass balance to climate change, as well as the uncertainty in present mass balance. Since the annual water exchange is about 8 mm of global sea level equivalent, the 20% uncertainty in current mass balance corresponds to 1.6 mm/yr in sea level change. Furthermore, estimates of the sensitivity of the mass balance to temperature change range from perhaps as much as - 10% to + 10% per K. A principal purpose of obtaining ice sheet elevation changes from satellite altimetry has been estimation of the current ice sheet mass balance. Limited information on ice sheet elevation change and their implications about mass balance have been reported by several investigators from radar altimetry (Seasat, Geosat, ERS-1&2). Analysis of ERS-1&2 data over Greenland for 7 years from 1992 to 1999 shows mixed patterns of ice elevation increases and decreases that are significant in terms of regional-scale mass balances. Observed seasonal and interannual variations in ice surface elevation are larger than previously expected because of seasonal and interannUal variations in precipitation, melting, and firn compaction. In the accumulation zone, the variations in firn compaction are modeled as a function of temperature leaving variations in precipitation and the mass balance trend. Significant interannual variations in elevation in some locations, in particular the difference in trends from 1992 to 1995 compared to 1995 to 1999, can be explained by changes in precipitation over Greenland. Over the 7 years, trends in elevation are mostly positive at higher elevations and negative at lower elevations. In addition, trends for the winter seasons (from a trend analysis through the average winter elevations) are more positive than the corresponding trends for the summer. At lower elevations, the 7-year trends in some locations are strongly negative for summer and near zero or slightly positive for winter. These observations also demonstrate the potential for relating the variability in mass balance to changes in precipitation, temperature, and melting. Beginning in January 2002, NASA's ICESat laser altimeter mission will provide significantly better elevation accuracy and spatial coverage to 86E latitude and to the margins of the ice sheets.

Zwally, H. Jay↗
NASA NTRS2002

Impact of Soil Moisture Initialization on Seasonal Weather Prediction

The potential role of soil moisture initialization in seasonal forecasting is illustrated through ensembles of simulations with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model. For each boreal summer during 1997-2001, we generated two 16-member ensembles of 3-month simulations. The first, "AMIP-style" ensemble establishes the degree to which a perfect prediction of SSTs would contribute to the seasonal prediction of precipitation and temperature over continents. The second ensemble is identical to the first, except that the land surface is also initialized with "realistic" soil moisture contents through the continuous prior application (within GCM simulations leading up to the start of the forecast period) of a daily observational precipitation data set and the associated avoidance of model drift through the scaling of all surface prognostic variables. A comparison of the two ensembles shows that soil moisture initialization has a statistically significant impact on summertime precipitation and temperature over only a handful of continental regions. These regions agree, to first order, with regions that satisfy three conditions: (1) a tendency toward large initial soil moisture anomalies, (2) a strong sensitivity of evaporation to soil moisture, and (3) a strong sensitivity of precipitation to evaporation. The degree to which the initialization improves forecasts relative to observations is mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies.

Koster, Randal D.↗
NASA NTRS2001

Thresholds in Atmosphere-Soil Moisture Interactions: Results from Climate Model Studies

The potential predictability of the effects of warm season soil moisture anomalies over the central U.S. has been investigated using a series of GCM (Global Climate Model) experiments with the NCAR (National Center for Atmospheric Research) CCM3 (Community Climate Model version 3)/LSM (Land Surface Model). Three different types of experiments have been made, all starting in either March (representing precursor conditions) or June (conditions at the onset of the warm season): (1) 'anomaly' runs with large, exaggerated initial soil moisture reductions, aimed at evaluating the physical mechanisms by which soil moisture can affect the atmosphere; (2) 'predictability' runs aimed at evaluating whether typical soil moisture initial anomalies (indicative of year-to-year variability) can have a significant effect, and if so, for how long; (3) 'threshold' runs aimed at evaluating if a soil moisture anomaly must be of a specific size (i.e., a threshold crossed) before a significant impact on the atmosphere is seen. The 'anomaly' runs show a large, long-lasting response in soil moisture and also quantities such as surface temperature, sea level pressure, and precipitation; effects persist for at least a year. The 'predictability' runs, on the other hand, show very little impact of the initial soil moisture anomalies on the subsequent evolution of soil moisture and other atmospheric parameters; internal variability is most important, with the initial state of the atmosphere (representing remote effects such as SST anomalies) playing a more minor role. The 'threshold' runs, devised to help resolve the dichotomy in 'anomaly' and 'predictability' results, suggest that, at least in CCM3/LSM, the vertical profile of soil moisture is the most important factor, and that deep soil zone anomalies exert a more powerful, long-lasting effect than do anomalies in the near surface soil zone. We therefore suggest that soil moisture feedbacks may be more important in explaining prolonged decadal to century-long droughts evident in the historic and recent prehistoric records, but less important on a seasonal to interannual time-scale.

Oglesby, Robert J.↗
NASA NTRS2017

GEOS-5 Seasonal Forecast System

Ensembles of numerical forecasts based on perturbed initial conditions have long been used to improve estimates of both weather and climate forecasts. The Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model, Version 5 (GEOS-5 AOGCM) Seasonal-to-Interannual Forecast System has been used routinely by the GMAO since 2008, the current version since 2012. A coupled reanalysis starting in 1980 provides the initial conditions for the 9 month experimental forecasts. Once a month, sea surface temperature from a suite of 11 ensemble forecasts is contributed to the North American Multi-Model Ensemble (NMME) consensus project, which compares and distributes seasonal forecasts of ENSO events. Since June 2013, GEOS-5 forecasts of the Arctic sea-ice distribution were provided to the Sea-Ice Outlook project. The seasonal forecast output data includes surface fields, atmospheric and ocean fields, as well as sea ice thickness and area, and soil moisture variables. The current paper aims to document the characteristics of the GEOS-5 seasonal forecast system and to highlight forecast biases and skills of selected variables (sea surface temperature, air temperature at 2 m, precipitation and sea ice extent) to be used as a benchmark for the future GMAO seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems.

GEOS-5↗
NASA NTRS2003

Circumpolar Circulation Patterns Over the Northern Hemisphere Oceans in Late Winter, 1949-2002.

To assess interannual changes in ocean-to-land advection, we extract zonal winds for February and March, 1949-2002, from NCEP/NCAR Reanalysis. Winds are analyzed at pairs of locations (55 deg N and 35 deg N) in the N. Pacific (15 deg W), N. Atlantic (30 deg W) and Baltic/Mediterranean (30 deg E). The monthly means at the northern and southern sites are negatively correlated. For N. Pacific, winds at 55 deg N show negative trends at all levels (magnitude increasing with altitude), versus positive at 35 deg N. An opposite scenario is observed over N. Atlantic, positive (negative) trends at 55 deg N (35 deg N) and similarly, but weaker, over the Baltic/Mediterranean. The geographic variability of trends is attributed to displacement of the polar vortex wave pattern. Increasing storm strength/frequency over N. Hemisphere oceans is inherently related to the strong positive trends in wind speed and vertical shear.

Otterman, J.↗
NASA NTRS2019

The Cumulus and Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat–CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc–Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness – as used in passive-sensor observations – is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).

Cesana, Gregory V.↗
NASA NTRS1997

Snowmelt on the Greenland Ice Sheet as Derived From Passive Microwave Satellite Data

The melt extent of the snow on the Greenland ice sheet is of considerable importance to the ice sheet's mass and energy balance, as well as Arctic and global climates. By comparing passive microwave satellite data to field observations, variations in melt extent have been detected by establishing melt thresholds in the cross-polarized gradient ratio (XPGR). The XPGR, defined as the normalized difference between the 19-GHz horizontal channel and the 37-GHz vertical channel of the Special Sensor Microwave/Imager (SSM/I), exploits the different effects of snow wetness on different frequencies and polarizations and establishes a distinct melt signal. Using this XPGR melt signal, seasonal and interannual variations in snowmelt extent of the ice sheet are studied. The melt is found to be most extensive on the western side of the ice sheet and peaks in late July. Moreover, there is a notable increasing trend in melt area between the years 1979 and 1991 of 4.4% per year, which came to an abrupt halt in 1992 after the eruption of Mt. Pinatubo. A similar trend is observed in the temperatures at six coastal stations. The relationship between the warming trend and increasing melt trend between 1979 and 1991 suggests that a 1 C temperature rise corresponds to an increase in melt area of 73000 sq km, which in general exceeds one standard deviation of the natural melt area variability.

Abdalati, Waleed↗
NASA NTRS2014

Climate Scenarios for the NASA / USAID SERVIR Project: Challenges for Multiple Planning Horizons

SERVIR, an acronym meaning "to serve" in Spanish, is a joint venture between NASA and the U.S. Agency for International Development (USAID) which provides satellite-based Earth observation data, modeling, and science applications to help developing nations in Central America, East Africa and the Himalayas improve environmental decision making. Anticipating climate variability / climate change impacts has now become an important component of the SERVIR efforts to build capacity in these regions. Uncertainty in hydrometeorological components of climate variations and exposure to extreme events across scales from weather to climate are of particular concern. We report here on work to construct scenarios or outlooks that are being developed as input drivers for decision support systems (DSSs) in a variety of settings. These DSSs are being developed jointly by a broad array NASA Applied Science Team (AST) Investigations and user communities in the three SERVIR Hub Regions, Central America, East Africa and the Himalayas. Issues span hydrologic / water resources modeling, agricultural productivity, and forest carbon reserves. The scenarios needed for these efforts encompass seasonal forecasts, interannual outlooks, and likely decadal / multi-decadal trends. Providing these scenarios across the different AST efforts enables some level of integration in considering regional responses to climate events. We will discuss a number of challenges in developing this continuum of scenarios including the identification and "mining" of predictability, addressing multiple continental regions, issues of downscaling global model integrations to regional / local applications (i.e. hydrologic and crop modeling). We compare / contrast the role of the U.S. National Multi- Model Experiment initiative in seasonal forecasts and the CMIP-5 climate model experiments in supporting these efforts. Examples of these scenarios, their use, and an assessment of their utility as well as limitations will be presented.

Robertson, Franklin R.↗
NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗
NASA NTRS2015

The Impact of Trends in the Large Scale Atmospheric Circulation on Mediterranean Surface Turbulent Heat Fluxes

Interannual variations of latent heat fluxes (LHF) and sensible heat fluxes (SHF) over the Mediterranean for the boreal winter season (DJF) show positive trends during 1958-2011. Comparison of correlations between the heat fluxes and the intensity and location of the Azores High (AH), and the NAO and East Atlantic-West Russia (EAWR) teleconnections, along with analysis of composites of surface temperature, humidity and wind fields for different teleconnection states, demonstrates that variations of the AH are found to explain the heat flux changes more successfully than the NAO and the EAWR. Trends in sea level pressure and longitude of the Azores High during DJF show a strengthening, and an eastward shift. DJF Azores High pressure and longitude are shown to co-vary such that variability of the Azores High occurs along an axis defined by lower pressure and westward location at one extreme, and higher pressure and eastward location at the other extreme. The shift of the Azores High from predominance of the low/west state to the high/east state induces trends in Mediterranean Sea surface winds, temperature and moisture. These, combined with sea surface warming trends, produce trends in wintertime Mediterranean Sea sensible and latent heat fluxes.

heat flux↗
NASA NTRS2004

Diurnal, Seasonal, and Interannual Variations of Cloud Properties Derived for CERES From Imager Data

Simultaneous measurement of the radiation and cloud fields on a global basis is a key component in the effort to understand and model the interaction between clouds and radiation at the top of the atmosphere, at the surface, and within the atmosphere. The NASA Clouds and Earth s Radiant Energy System (CERES) Project, begun in 1998, is meeting this need. Broadband shortwave (SW) and longwave radiance measurements taken by the CERES scanners at resolutions between 10 and 20 km on the Tropical Rainfall Measuring Mission (TRMM), Terra, and Aqua satellites are matched to simultaneous retrievals of cloud height, phase, particle size, water path, and optical depth OD from the TRMM Visible Infrared Scanner (VIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. Besides aiding the interpretation of the broadband radiances, the CERES cloud properties are valuable for understanding cloud variations at a variety of scales. In this paper, the resulting CERES cloud data taken to date are averaged at several temporal scales to examine the temporal and spatial variability of the cloud properties on a global scale at a 1 resolution.

Minnis, Patrick↗
NASA NTRS1995

Interdecadal Variations in the Alaska Gyre

Climatic dynamic topography variations in the Alaska gyre during the period 1968-1990 are described with an objective analysis of more than 12000 STD and XBT stations, and COADS wind stress data Interannual the dynamic height and SST variations were correlated and were consistent with recently described large-scale climatic shifts in the North Pacific. The gyre was centered more to the east, circulation appeared stronger, and SST was lower during the early to mid-1970s than during the 1980s. The Aleutian low (NP and PNA indices) intensified during the interim, but the response did not appear as a gyre spinup. Instead, the associated wind stress anomalies forced a slowly varying dynamic height anomaly across the eastern and northern part of the gyre through Ekman convergence, which had the effect of displacing the gyre's low somewhat to the WSW in the 1980s. The wind curl spectrum was white, and the slow oceanic response was modeled as stochastic-forced climate variability with a simple first-order Markov autoregression process. Forcing was assumed to be Ekman pumping of the pycnocline, and the damping coefficient was estimated from the data to be approx. 1 yr. A hindcast with observed winds gave estimated dynamic height patterns similar to those observed, with a canonical correlation of 0.79 at 99% confidence. This response was weak in the western half of the gyre, where slow baroclinic variability may have been influenced by long Rossby wave propagation. A simple autoregression simulation using artificial white noise forcing shows the evolution of decadal variations similar in nature to those observed. This result, along with the low frequency correlation between dynamic height and SST, suggests that the upper-ocean climatic variability in this region is primarily wind forced.

Lagerloef, Gary S.E.↗
NASA NTRS1998

Trends in Antarctic Surface Temperatures and Ice Extent from Satellite and Surface Observations

Surface air temperatures observed from stations around Antarctica have indicated predominantly positive trends which are as high as 0.4K per decade along the Antarctic Peninsula. Since the average air temperature during summer in the West Antarctic region is about -6 K, and since most of the stations are near coastal regions, it is important to know whether such trends are due to a local or large scale phenomenon. We analyzed about 19 years of infrared satellite data (1979-1997) to obtain a spatially detailed study of the variability of surface temperatures over the entire Antarctic region. We also analyzed potential impact of temperature changes over the sea ice cover using co-registered and nearly coincident microwave satellite data. The surface temperatures inferred from infrared data are shown to agree well with those observed from the limited number of Antarctic station data with a correlation coefficient of 0.98 and a standard deviation of less than 3K. The 19-year temperature data set shows positive trends of 0.12, 0.68, and 0.41 C per decade in ocean, sea ice and ice sheet surfaces, respectively. The sensitivity of the trend to record length was studied, using long term station data sets, and the results show that the trends start to stabilize at around 2 decades which is about the record length of satellite data. However, interannual fluctuations of the temperatures are large, especially in the ice sheets, and the 95% confidence level for the trends ranges from 0.12 to 1.22 for sea ice and from -0.74 to 1.55 C per decade for the ice sheet. In the sea ice regions, the position of the ice edge in winter with respect to the northernmost position of sea ice during the study period is also shown to be influenced by alternating warm and cold anomalies around the continent. This pattern is similar to that suggested by the presence of the Antarctic Circumpolar Wave (ACW) but the wavenumber appears to be predominantly mode 3 (instead of reported mode 2) for the Antarctic region. Unexpectedly, trends in the sea ice extent are found to be positive in most Antarctic sectors during the same period with the exception of the Bellingshausen and Amundsen Seas region.

Comisc, J. C.↗
NASA NTRS2003

Causes of Long-Term Drought in the United States Great Plains

This study examines the causes of long term droughts in the United States Great Plains (USGP). The focus is on the relative roles of slowly varying SSTs and interactions with soil moisture. The results from ensembles of long term (1930-1999) simulations carried out with the NASA Seasonal-to- Interannual Prediction Project (NSIPP-1) atmospheric general circulation model (AGCM) show that the SSTs account for about 1/3 of the total low frequency rainfall variance in the USGP. Results from idealized experiments with climatological SST suggest that the remaining low frequency variance in the USGP precipitation is the result of interactions with soil moisture. In particular, simulations with soil moisture feedback show a five-fold increase in the variance in annual USGP precipitation compared with simulations in which the soil feedback is excluded. In addition to increasing variance, the interactions with the soil introduce year-to-year memory in the hydrological cycle that is consistent with a red noise process, in which the deep soil is forced by white noise and damped with a time scale of about 2 years. As such, the role of low frequency SST variability is to introduce a bias to the net forcing on the soil moisture that drives the random process preferentially to either wet or dry conditions.

Schubert, Siegfried D.↗
NASA NTRSDate not supplied

Inferred Sea Level Prediction in the NASA GMAO Seasonal Forecasting System

Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.

Richard I Cullather↗
NASA NTRS2004

ENSO Bred Vectors in Coupled Ocean-Atmosphere General Circulation Models

The breeding method has been implemented in the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Coupled General Circulation Model (CGCM) with the goal of improving operational seasonal to interannual climate predictions through ensemble forecasting and data assimilation. The coupled instability as cap'tured by the breeding method is the first attempt to isolate the evolving ENSO instability and its corresponding global atmospheric response in a fully coupled ocean-atmosphere GCM. Our results show that the growth rate of the coupled bred vectors (BV) peaks at about 3 months before a background ENSO event. The dominant growing BV modes are reminiscent of the background ENSO anomalies and show a strong tropical response with wind/SST/thermocline interrelated in a manner similar to the background ENSO mode. They exhibit larger amplitudes in the eastern tropical Pacific, reflecting the natural dynamical sensitivity associated with the presence of the shallow thermocline. Moreover, the extratropical perturbations associated with these coupled BV modes reveal the variations related to the atmospheric teleconnection patterns associated with background ENSO variability, e.g. over the North Pacific and North America. A similar experiment was carried out with the NCEP/CFS03 CGCM. Comparisons between bred vectors from the NSIPP CGCM and NCEP/CFS03 CGCM demonstrate the robustness of the results. Our results strongly suggest that the breeding method can serve as a natural filter to identify the slowly varying, coupled instabilities in a coupled GCM, which can be used to construct ensemble perturbations for ensemble forecasts and to estimate the coupled background error covariance for coupled data assimilation.

Yang, S. C.↗
NASA NTRS2005

Variability of the ocean-induced magnetic field predicted at sea surface and at satellite altitudes

Spatial and temporal variability of the magnetic field component induced by ocean circulation is investigated on the basis of a standard thin-shell approximation of electro- and magneto-static equations. Well-known difficulties of numerical solution of the governing equations are resolved by reducing the problem to an equation for the electric field potential,(ef) as opposed to a more conventional approach focused on the vertical jump, (psi), of the magnetic field potential across a combined ocean/ marine-sediment-layer spherical shell. The present formulation permits using more realistic input data on ocean currents and ultimately yields much greater (by at least an order of magnitude) values of the magnetic field at sea surface than predicted in earlier studies. Such large values are comparable to, and in some cases exceed, magnetic field variations caused by lithospheric and ionospheric sources on monthly to interannual timescales. At the 400-km altitude (of CHAMP satellite), the field attains 6 nT. The model predictions show favorable comparisons with some in situ measurements as well as with Challenging Minisatellite Payload (CHAMP) satellite magnetometer data.

ocean induced magnetic field↗
NASA NTRS2013

Flowering Date of Taxonomic Families Predicts Phenological Sensitivity to Temperature: Implications for Forecasting the Effects of Climate Change on Unstudied Taxa

Premise of the study: Numerous long-term studies in seasonal habitats have tracked interannual variation in fi rst fl owering date (FFD) in relation to climate, documenting the effect of warming on the FFD of many species. Despite these efforts, long-term phenological observations are still lacking for many species. If we could forecast responses based on taxonomic affi nity, however, then we could leverage existing data to predict the climate-related phenological shifts of many taxa not yet studied; Methods: We examined phenological time series of 1226 species occurrences (1031 unique species in 119 families) across seven sites in North America and England to determine whether family membership (or family mean FFD) predicts the sensitivity of FFD to standardized interannual changes in temperature and precipitation during seasonal periods before fl owering and whether families differ signifi cantly in the direction of their phenological shifts; Key results: Patterns observed among species within and across sites are mirrored among family means across sites; earlyfl owering families advance their FFD in response to warming more than late-fl owering families. By contrast, we found no consistent relationships among taxa between mean FFD and sensitivity to precipitation as measured here; Conclusions: Family membership can be used to identify taxa of high and low sensitivity to temperature within the seasonal, temperate zone plant communities analyzed here. The high sensitivity of early-fl owering families (and the absence of earlyfl owering families not sensitive to temperature) may refl ect plasticity in fl owering time, which may be adaptive in environments where early-season conditions are highly variable among years.

first flowering date↗